Method and apparatus for multidomain data analysis

ABSTRACT

An optical measuring device generates a plurality of measured optical data from inspection of a thin film stack. The measured optical data group naturally into several domains. In turn the thin film parameters associated with the data fall into two categories: local and global. Local “genes” represent parameters that are associated with only one domain, while global genes represent parameters that are associated with multiple domains. A processor evolves models for the data associated with each domain, which models are compared to the measured data, and a “best fit” solution is provided as the result. Each model of theoretical data is represented by an underlying “genotype” which is an ordered set of the genes. For each domain a “population” of genotypes is evolved through the use of a genetic algorithm. The global genes are allowed to “migrate” among multiple domains during the evolution process. Each genotype has a fitness associated therewith based on how much the theoretical data predicted by the genotype differs from the measured data. During the evolution process, individual genotypes are selected based on fitness, then a genetic operation is performed on the selected genotypes to produce new genotypes. Multiple generations of genotypes are evolved until an acceptable solution is obtained or other termination criterion is satisfied.

This application is a continuation of application Ser. No. 09/542,724,filed Apr. 4, 2000, now U.S. Pat. No. 6,532,076, entitled “METHOD ANDAPPARATUS FOR MULTIDOMAIN DATA ANALYSIS.”

FIELD OF THE INVENTION

This invention relates to a multidomain method for evaluating theformation of thin films on semiconductor substrates using opticalmethods, and an apparatus embodying the method.

BACKGROUND OF THE INVENTION

Optical methods for measuring samples are generally known, inparticular, for semiconductor fabrication involving the formation of astack of thin film layers on a semiconductor substrate. Such methods areconsidered essential for the efficient operation of modern fabricationfacilities. Optical methods are desirable because they arenon-destructive and the resultant optical data can be used to deriveinformation regarding layer parameters, such as thickness, refractiveindex, extinction coefficient, dispersion and scattering, for multiplelayers of a thin film stack.

One preferred approach includes the use of the OPTIPROBE detectormanufactured and sold by Therma-Wave, Inc. of Fremont, Calif., assigneeherein, and described in part in one or more of the following U.S. Pat.Nos. 4,999,014; 5,042,951; 5,181,080; 5,412,473; and PCT publication WO99/02970, each of which is incorporated herein by reference in itsentirety.

Conventional optical processing technology typically relies upon using anon-linear least squares algorithm to fit the measured data to a set ofdata points with a solution representing specific parameters of a thinfilm stack.

Improvements in optical technologies can provide an ever-increasingnumber of measured data points, which in turn provide the opportunityfor deriving layer parameters on more complicated film stacks. However,this opportunity also presents a more complex optimization problem fordeveloping solutions based on the observed data, and conventionalprocessing techniques (such as least squares algorithms) are inadequateto handle the increased complexity.

Genetic Algorithms (GA's) have been applied to the problem of adaptivefunction optimization. A basic theoretical framework for GA's isdescribed in Holland, Adaptation in Natural and Artificial Systems(1975). The terminology used by Holland is borrowed from genetics. Thus,in the computer analog, a GA is a method for defining a “population” ofsolutions to a selected problem, then evolving new populations by usingprobabilistic genetic operations to act on “individual” members of thepopulation, i.e. individual solutions. Each individual in the populationhas a plurality of “genes,” which are each representative of some realparameter of interest. For example, if there are x data parameters ofinterest, each individual would have x genes, and populations ofindividuals having x genes would be propagated by a GA.

The use of GA's for function optimization is generally described in U.S.Pat. No. 5,222,192 and U.S. Pat. No. 5,255,345, both to Schaefer.Further, U.S. Pat. No. 5,394,509 to Winston generally describes theapplication of GA's to search for improved results from a manufacturingprocess. Also, there has recently been much interest in the use of GA'sin the design of various types of optical filters. See Eisenhammer, etal., Optimization of Interference Filters with Genetic AlgorithmsApplied to Silver-Based Heat Mirrors, Applied Optics, Vol. 32 at pp.6310-15 (1993); and Bäck & Schütz, Evolution Strategies forMixed-Integer Optimization of Optical Multilayer Systems, Proceedings ofthe Fourth Annual Conference on Evolutionary Programming at pp. 33-51(1995).

More recently, GA's have been applied to the problem of evaluating thinfilms on semiconductor wafers. U.S. Pat. No. 5,864,633, herebyincorporated by reference in its entirety, describes the application ofGA's to the problem of evaluating the characteristics of thin filmlayers with an optical inspection device. The present invention isdirected to an improvement on the method disclosed in U.S. Pat. No.5,864,633 involving a multidomain optimization technique.

SUMMARY OF THE INVENTION

The technique described in U.S. Pat. No. 5,864,633 relates to aninvention useful for converting optical measurements at a point on asemiconductor wafer into a description of the thin films beneath thatpoint on the wafer. This application describes a modification of the GAtechnique described in U.S. Pat. No. 5,864,633 in order to improveeither the evaluation of wafer measurements at multiple points on awafer or the evaluation of (possibly multiple) measurements of multiplewafers.

The present invention provides a suitable multidomain optimizationtechnique for doing this wherein two or more populations of genotypesare used. In general one evolving genotype population is employed foreach of the individual measurement points on the wafer. Flags are usedto divide the genes of the genotypes into two categories or classes:local and global. Local genes represent parameters that are onlyassociated with one domain whereas global genes represent parametersassociated with more than one domain. More than one category of globalgene may be employed.

By subdividing the genes into global and local categories and employinga migration step that allows genotypes to move among two or moredomains, the present invention improves the evaluation of the samplecompared with the method taught in U.S. Pat. No. 5,864,633. This is sobecause the present invention allows the optimization process to reflectthe possibility that some parameters may be constant or nearly constantacross multiple domains whereas others may not be.

Multiple generations of the genotypes in each domain are evolved untilan acceptable solution is obtained. Using conventional Fresnelequations, a processor derives theoretical data from the theoreticalparameters defining each of the genotypes. The derived theoretical datafor a given genotype are compared with the actual measured data inaccordance with a fitness function. The fitness function provides ameasure of how close the derived theoretical data are to the measureddata. Individual genotypes are then selected based on this fitnesscomparison. Genetic operations are performed on the selected genotypesto produce new genotypes. In addition to crossover, direct reproduction,and mutation operations, a migration step is performed that allowsgenotypes to migrate among two or more domains.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating a simplified optical inspectionsystem.

FIG. 2 shows a thin film sample on which a linescan of measurements hasbeen made at 10 different points.

FIG. 3 shows a flow chart illustrating a portion of a method accordingto present invention.

FIG. 4 shows a flow chart illustrating an additional portion of themethod shown in FIG. 3 according to present invention.

FIGS. 5A through 5C are flow chart portions illustrating the use ofdifferent genetic operations.

FIG. 6 shows a flow chart illustrating an alternative method accordingto the present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 illustrates a block diagram for a basic optical inspection system20 for measuring optical characteristics of a sample 28, such as asemiconductor wafer having one or more thin film layers 32 formedthereon. A light source 22 generates a probe beam of light 24 which isreflected by beam splitter 26 through lens 30 onto the sample 28. Theprobe beam of light 24 is reflected off the sample 28 back through lens30 and beam splitter 26 onto a photodetector 50. Photodetector 50generates a plurality of outputs 51 which are supplied to a processor52. The outputs are used to evaluate physical characteristics of thesample, and more particularly, of the thin film layer(s) 32 of thesample.

It will be appreciated by those skilled in the art that manyconfigurations for an optical inspection system are possible, such asthose described in U.S. Pat. No. 4,999,014; U.S. Pat. No. 5,042,951;U.S. Pat. No. 5,181,080; U.S. Pat. No. 5,412,473; and PCT publication WO99/02970, each of which is incorporated herein by reference in itsentirety. The preferred optical inspection system employs the OPTIPROBEdetector manufactured and sold by Therma-Wave, Inc. of Fremont, Calif.These patents describe how measurements may be taken at multiplewavelengths and at multiple angles of incidence either simultaneously orserially. However, for the purpose of the present invention, it issufficient to have an optical inspection system which generates multipleoptical data measurements from the inspection of the semiconductorwafer.

An inspection system based on X-rays could also be used to generate thedata measurements. A preferred X-ray inspection system is described inU.S. Pat. No. 5,619,548, issued Apr. 8, 1997, which is herebyincorporated by reference in its entirety.

The optical data measurements will typically take the form of amplitudeinformation, such as reflectance versus angle of incidence, orreflectance versus wavelength, or polarization phase information, suchas provided by ellipsometry. For example, the OPTIPROBE detector useseach of these techniques to take a large number of measurements in asingle scan, then it filters the measured data resulting in from tens tohundreds of data points for each set of measurements.

Well known Fresnel equations can be used to predict or model the opticalmeasurements expected from a known stack of layers with specifiedthicknesses, reflection indices and extinction coefficients. See Born &Wolf, Principles of Optics. However, the nature of the Fresnel equationsis such that they cannot be easily inverted in order to unambiguouslydetermine the various parameters of a thin film stack from a largenumber of multiple measured data points.

For this reason it is not easy to associate a correct set of theoreticalparameters with measured data. Given a set of “theoretical” parameterswhich might correspond to the actual parameters of the stack to beevaluated, one can program a processor, using the Fresnel equations, toderive a set of theoretical data based on these theoretical parameters.The derived theoretical data may then be compared to the measured dataand if there is a reasonable level of correspondence, one can assumethat the generated theoretical parameters fairly describe the parametersof the thin film stack under investigation.

Of course, it would be highly unlikely that the first set of generatedtheoretical parameters, and the associated derived theoretical data,would provide a good match to the actual measured data. In the practiceof the invention, the processor will generate thousands of sets oftheoretical parameters. In accordance with the subject invention, thesteps of generating the multiple sets of theoretical parameters areperformed using a genetic algorithm.

FIG. 2 illustrates an example of the type of problem that might beaddressed by the present invention. FIG. 2 shows a sample with adielectric thin film layer 80 on a substrate 60. The thickness of thethin film layer 80 increases in a continuous but unknown manner alongthe length of the sample. By way of example, one may wish to determinethe thicknesses T_(i) and indices of refraction N_(i) of the thin filmlayer at multiple points on the sample rather than a single point. Givena linescan measurement that provides data at ten different points on thewafer, as shown in FIG. 2, it is desirable to ultimately parameterizethe thin film thickness estimates on a point by point basis (10parameters), but yet to ultimately parameterize a single value for thethin film index of refraction over all ten points (1 parameter).

According to the present invention, one optimizes the design of thegenotypes to reflect the multidomain nature of the problem that one isaddressing.

In the case of the FIG. 2 example, one would employ 10 domains orgenotype populations, one corresponding to each point for which data wascollected during the linescan. A genotype may be defined as an orderedset of genes, each gene representing a different thin film parameter ofinterest. In this example, each genotype would consist of an orderedpair of values T_(i) and N_(i), representing the thickness and index ofrefraction of the thin film layer at a given point, respectively. Theinitial population of genotypes for each domain can be varied based onthe complexity of the problem and the amount of computation time andpower available. By way of illustration, an initial population of 100genotypes might be chosen for each of the 10 domains in the FIG. 2example.

In accordance with the present invention, one classifies the individualgenes as local or global. More than two categories of genes could alsobe employed. During the evolutionary GA process the global genes will beallowed to migrate among two or more domains. In the FIG. 2 problem, forexample, one would classify the genes representing thickness as localand the genes representing index of refraction as global, since it maybe assumed that the index is relatively constant. Typically theselection of genotypes for migration would be based partly on thefitness of the genotypes in which they occur and partly on chance. Inother words, while random, the selection process would typically beweighted according to gentotype fitness. Migration of genotypes from agiven domain associated with a given measurement point on the samplecould be allowed to all other 9 domains or could be limited to domainsassociated with “neighboring” measurement points. Typically migrantgenotypes received in a new domain would replace the least fit genotypesin the new domain. Alternatively, rather than migrating entire selectedgenotypes, the global genes from selected genotypes in a given domaincould be used to replace corresponding global genes in selectedgenotypes in a new domain. In this alternative, then, global genesthemselves, rather than entire genotypes, would in effect “migrate” byreplacing the corresponding genes in selected genotypes in a new domain.

The GA process may be allowed to evolve until some termination criterionis satisfied. Such a termination criterion could involve the setting ofa maximum number of generations, or a test for relative stability amongthe most fit, “elite” genotypes, or both. One expects that, as thedomains evolve under the GA, the global genes of the elite genotypes inthe different domains will tend to converge in value. In the case of theFIG. 2 problem, one would expect that at the end of the GA process therange of values for the global genes of the elite genotypes of the 10domains would be much narrower than the range of values for the localgenes of the elite genotypes. This reflects the physical fact that alongthe sample there is less variance in the index of refraction in the FIG.2 thin film layer 80 than in its thickness.

Although the present invention is described in the context of geneticsearch algorithms, it could just also be applied to other kinds ofiterative search algorithms (e.g., a neural network) that might be usedto solve the multidomain problem of finding optimal parameter values fora semiconductor wafer sample. Given optical measurements made atdifferent data points on a semiconductor wafer or wafer set, as long assome of the parameters are local in nature so that they should varysignificantly between the data points, one would want to improve thesearch process by defining multiple search domains corresponding to thedifferent measurement data points. And as long as some of the parametersare global in nature so that they should vary little between the datapoints, one would want to apply some type of cross-talk operation toimprove the search process by communicating information about the globalparameters among the different search domains as the search processproceeds.

Referring now to FIG. 3, a flow chart illustrates one method, which maybe implemented in processor 52 with suitable programming, to carry outthe present invention. It should be recognized that many variations inmethodology could be used without affecting the scope of this invention.Processor 52 can be any general purpose computer having adequatecomputing resources for performing iterative processing. For example, aPENTIUM III processor running the LINUX operating system could be used.

In step 98, the set of thin film parameters to be optimized are chosenand mapped into a genotype. In practical terms, each parameter ofinterest or gene can be mapped into an individual data store, its rangespecified, and its contents supplied, altered, or otherwise operated onin accordance with suitable programming. The collection of individualdata stores which store all the parameters of interest for a givenmeasurement constitutes an individual genotype. It can be appreciatedthat the genotypes may be handled with common data processing commandsto operate on the information stored therein for any suitable purpose.The present invention employs a GA to operate on selected genes topropagate additional genotypes having generally increasing fitness.

In addition in step 98 the genes are classified into local and globalcategories. As described above, global genes will be allowed to migrateamong two or more domains. More than two categories of genes may beemployed with the different categories of genes having different levelsof migration privileges.

In step 99, a group of more than one initial populations, eachcomprising M_(p) individual genotypes is created either at random or byarbitrary means. For example, the initial p populations may beinitialized with preexisting data from prior measurements. The number ofinitial genotypes M_(p) “seeded” into each of the p genotype populationsis also arbitrary and may be chosen in light of the computer poweravailable and the complexity of the problem.

The fitness of each genotype in the current populations is evaluated andstored for reference in step 100. The fitness is determined by a fitnessfunction F, which is based on the parameters of interest. The fitness Fmay be defined as a function of the residual value between a measureddata point x_(i) and a theoretical data point y_(i), for N measurements,for example: ${F = ({RES})},{{e.g.2} - \sqrt{RES}}$ where${RES} = \sqrt{\left( {1/N} \right){\sum\limits_{i = 1}^{N}\left( {x_{i} - y_{i}} \right)^{2}}}$

One way in which the fitness F may be measured is to apply the Fresnelequations to the parameters of a genotype to get predicted values thatcan then be compared with the measurement values. Another way to measurefitness is to take the parameters of the genotypes as the starting pointfor performing an iterative nonlinear least squares optimizationtechnique such as the well-known Marquardt-Levenberg algorithm. Fitnessis then measured by comparing the values predicted by the results of thenonlinear least squares optimization technique to the measurementvalues. A suitable iterative optimization technique for this purpose isdescribed in “Multiparameter Measurements of Thin Films UsingBeam-Profile Reflectivity,” Fanton et al., Journal of Applied Physics,Vol. 73, No. 11. p.7035 (1993) and “Simultaneous Measurement of SixLayers in a Silicon on Insulator Film Stack Using Spectrophotometry andBeam Profile Reflectometry,” Leng et al., Journal of Applied Physics,Vol. 81, No. 8, p.3570 (1997). These two articles are herebyincorporated by reference in their entireties. Fitness could also bemeasured using the Fresnel equations directly sometimes and using anonlinear optimization technique at other times.

The variable GEN is used to identify the generation number and is theninitialized to zero.

In step 101, termination criteria are examined, and if the criteria aresatisfied, a preferred solution results in the methodology of FIG. 4being applied (“point D”).

The selection of genotypes in step 102 is statistically based upon howclosely the theoretical data associated with the genotypes fits with themeasured data. The selection is such that it is more likely that agenotype having a high fitness will be selected than one having a lowfitness. This type of selection process is sometimes called a weightedlottery. In the preferred embodiment, a newly migrated genotype mustreside in the new population for at least one generation before it canbe selected again for migration via step 102.

Step 103 moves the selected, migrant genotype to another populationdomain. The population that receives the migrant genotype may beselected at random or may be restricted to some subset such as the“nearest neighbor” populations. The genotype with the worst fitness inthe selected population domain is replaced with the migrant genotype, solong as the replaced genotype is not a recent migrant. If the genotypewith the worst fitness is a recent migrant, then the genotype with theworst fitness among those genotypes that are not recent migrants may beused for replacement. In an alternative embodiment, rather thanmigrating entire selected genotypes, the global genes from selectedgenotypes in a given domain could be used to replace the global genes ingenotypes in a new domain. In this alternative, then, global genesthemselves, rather than entire genotypes, would in effect migrate.

In step 104 the fitness of the newly received migrants in each domain isevaluated in the same manner as was done in step 100.

In step 105, counters for each domain, such as counter i, are reset tozero. Counter i counts the number of genotypes which are created in thenext generation population. In this part of the routine a new populationof genotypes is propagated in each population domain through geneticoperations and forms the next generation. In order to count the numberof genotypes in the population domain, a counter is initialized for eachpopulation domain and thereafter serves to track the number of genotypeswhich are genetically propagated in the bottom portion of the routine.

In step 106, the counters are compared to the preset values M_(p). Onceall the counters for the populations p are equal to the correspondingM_(p) values, then the new populations are full and the generationnumber GEN is incremented by one in step 114. The routine then returnsto step 101 to either terminate or begin constructing another generationof genotypes. If the new populations are not full, the routine proceedsto step 108 and evolves one or more new genotypes for the newgeneration.

In step 108, a genetic operation is selected. The selection amonggenetic operations will usually be made probabilistically, but could beperformed arbitrarily. There are three basic genetic operations, namelydirect reproduction, crossover and mutation, as illustrated in FIGS. 5A,5B, and 5C, although the invention is not strictly limited in thissense. Each of these genetic operations should be employed to somedegree to provide a sufficiently random evolution of the genotypes,although this is not strictly required.

For each of the three possible genetic operations, either one or twogenotypes are selected from the current population. The genotypeselected in step 109 is statistically based upon how closely thetheoretical data associated with that genotype “fits” with the measureddata. Although the selection is by chance, it is more likely that agenotype having a high fitness F will be selected than one having a lowfitness. In the preferred embodiment, the likelihood of being selectedis directly proportional to the fitness. By selecting the genotypes inthis Darwinian fashion, the population can evolve in a manner so thatthe genotypes migrate towards progressively better fitting solutions. Inaddition, by using a weighted, but still random selection process, it ispossible to search for best fit solutions over the entire population.This provides a more efficient search of the total solution space thancan be achieved using nonlinear least square fitting algorithms that usesearch strategies that by their nature are much more localized.

The chosen form of genetic operation will be carried out in step 110.The new genotype(s) created by the genetic operation are then writteninto the new population in step 111, and the counter for the respectivepopulation domain is incremented in step 112.

Steps 108 through 112 may be carried out in many different ways withoutdeparting from the scope of the invention. For example, the three basicgenetic operations are illustrated in FIGS. 5A-5C. If directreproduction is chosen in step 108 a, a single genotype is selected instep 109 a. As noted above, this selection is random, but weighted basedon fitness. In step 110 a, an exact copy of that selected genotype iscopied and inserted into a new population (step 111 a). The individualcounter i for the respective population domain p is then incremented instep 112 a and the routine returns to step 106 to propagate moregenotypes until the new population is full. Alternatively, the exactcopy of the selected genotype may be subjected to genetic mutationbefore being copied into the new population, as indicated by the dottedline connection B to step 109 c.

If crossover is selected in step 108 b, then two genotypes are randomlychosen from the current population (step 109 b) based on their fitness.Crossover is then carried out in step 110 b, meaning that genes fromeach of the selected genotypes are selected and exchanged, therebyforming two new genotypes which are then written into the new populationin step 111 b. If crossover is selected, the individual counter i forthe respective population domain must be incremented twice in step 112 bsince two new genotypes are evolved. The routine returns to step 106 topropagate more genotypes until the new population is full.Alternatively, the crossover genotypes may be subjected to geneticmutation before being copied into the new population, as indicated bythe dotted line connection B to step 109 c.

If mutation is selected, then one genotype is chosen from the currentpopulation in step 109 c based on its fitness. Some number of genes fromthe selected genotype are selected and then mutated in step 110 c, andthe new genotype is written into the new population in step 111 c. Theindividual counter i is then incremented in step 112 and the routinereturns to step 106 to propagate more genotypes until the new populationis full at which point the generation counter GEN is incremented.

As noted above, the selection of genotypes for use in the geneticoperation is generally according to a weighted lottery based on fitness,although it is possible to force a selection through directintervention. Also, the selection of individual genes to be operatedupon is generally random.

As previously discussed, the routine will run until a terminationcriterion is satisfied in step 101. In practice, the terminationcriterion is designed to allow the population to evolve for apredetermined number of generations. In this case, such a predeterminednumber can be selected based on how fast the processor runs and how longthe operator is willing to wait for a result. It should be understoodthat the longer the populations are allowed to evolve, the more likelyit is that a good fit will be obtained. Other termination criteria couldbe established, such as when the fitness of the best genotype of thepopulation does not improve by at least some selected amount δ over thelast Q generations. When the termination criteria is satisfied, anotherroutine shown in FIG. 4 is applied (“point D” of FIG. 3).

The routine that begins at point D can be described as follows. In loops300 and 400, one takes the best genotype from each population domain andfinds the average of each global parameter. In other words, for eachglobal parameter one sums the corresponding gene values of the bestgenotypes from each domain in loop 300 and then divides by the number ofdomains (“N”) to yield a global average in loop 400. For the bestgenotype in each domain, the gene values corresponding to each globalparameter are then reset to this global average also in loop 400. Thenin loop 500 one takes the best genotype for each domain and performs anonlinear least squares optimization for the genes in that genotype thatcorrespond to the local parameters. At the end of this process, for eachdomain, one has a solution genotype whose global parameter genes are anaverage of those for the best genotypes of all of the domains and whoselocal parameter genes are the result of the nonlinear optimizationprocess. As described above, a suitable iterative optimization techniquefor this purpose is disclosed in “Multiparameter Measurements of ThinFilms Using Beam-Profile Reflectivity,” Fanton et al., Journal ofApplied Physics, Vol. 73, No. 11. p.7035 (1993) and “SimultaneousMeasurement of Six Layers in a Silicon on Insulator Film Stack UsingSpectrophotometry and Beam Profile Reflectometry,” Leng et al., Journalof Applied Physics, Vol. 81, No. 8, p.3570 (1997), which areincorporated by reference.

An alternative to the method shown in loop 500 of FIG. 4 would be toagain run a genetic algorithm in each domain during which algorithm theglobal genes would be held fixed and only the local genes would beallowed to evolve.

One expects that, as the domains evolve under the GA, the global genesof the elite genotypes in the different domains will tend to converge invalue. For each global parameter, one could test the associated globalgenes for such a convergence at each generation and then limit theallowed range of values for those global genes based on the degree ofconvergence obtained thus far. This could be done simply by resettinggene values falling above the allowed range to the range's maximumvalue, and gene values falling below the allowed range to the range'sminimum value. In other words the allowed “search range” for the globalgenes may be dynamically narrowed as the GA proceeds. This alternativeembodiment is shown in FIG. 6 in which the added step 113 prior to theincrementing of the GEN counter represents such a dynamic narrowing ofthe search range for the global genes as the GA proceeds. Aftertermination of the GA, as indicated by point D′, one may optionallyproceed to optimize the local genes of the best genotypes in each domainwith a nonlinear least squares algorithm as is shown in loop 500 of FIG.4.

It should be understood that the invention is not intended to be limitedby the specifics of the above-described embodiment, but rather definedby the accompanying claims.

We claim:
 1. A multidomain process for evaluating parameters of asemiconductor wafer or wafer set comprising: generating measured datafor multiple points on the semiconductor wafer or wafer set; definingmultiple domains, wherein a domain has corresponding measured data;applying an iterative search method to each domain in order to generatea group of optimized semiconductor wafer parameter values for eachdomain, said iterative search method including: generating a pluralityof sets of theoretical semiconductor wafer parameter values, andderiving a set of theoretical measurement data for each set oftheoretical semiconductor wafer parameter values; associating differentsets of theoretical semiconductor wafer parameter values with eachdomain; comparing a first set of theoretical measurement data, derivedfrom a set of theoretical semiconductor wafer parameter valuesassociated with a domain, to the measured data corresponding to thedomain; and iteratively generating a new set of theoreticalsemiconductor wafer parameter values to be associated with the domainbased on the comparing in a manner so as to associate increasingly moreoptimal theoretical semiconductor wafer parameter values with thedomain, wherein the generation of the new set of theoreticalsemiconductor parameter values includes migrating at least onetheoretical semiconductor parameter value from a set of theoreticalsemiconductor parameter values that has been associated with a differentdomain.
 2. The method of claim 1, wherein the applying an iterativesearch method includes generating new theoretical semiconductor waferparameter values in accordance with a genetic algorithm.
 3. The methodof claim 1, wherein said generating a plurality of sets of theoreticalwafer parameter values includes defining a plurality of genotypeswherein a genotype includes a plurality of genes, wherein the pluralityof genes correspond to different wafer parameters.
 4. The method ofclaim 3, wherein one or more of the plurality of genes is defined asbelonging to a first class where genes of the first class are optimizedfor each domain, and one or more of the plurality of genes is defined asbelonging to a second class where genes of the second class areoptimized across a plurality of domains, and one or more of theplurality genes is defined as belonging to a third class where genes ofthe third class are optimized across all domains, such that the genes ofthe third class are deemed to have the same value for each domain. 5.The method of claim 3, wherein each of the genes of the plurality ofgenes is defined as belonging to one class of a group of different geneclasses, wherein the group of different gene classes includes a firstclass where genes of the first class are optimized for each domain, asecond class where genes of the second class are optimized across aplurality of domains, and a third class where genes of the third classare optimized across all domains, such that the genes of the third classare deemed to have the same value for each domain.
 6. A multidomainprocess for evaluating parameters of a semiconductor wafer or wafer setcomprising: identifying a group of semiconductor wafer parameters to beevaluated; mapping the semiconductor wafer parameters into at least onegenotype, said genotype comprising a collection of genes, each genecorresponding to a selected one of the group of semiconductor waferparameters to be evaluated, wherein each gene belongs to one of aplurality of gene classes; defining more than one domain as a collectionof genotypes, each domain having its own population of genotypes;comparing a set of measurement data to a corresponding set oftheoretical data derived for each genotype in each domain in order todetermine a level of fitness for each genotype; iteratively modifying agenotype population of a domain so that a correspondence betweenmeasurement data and the theoretical data derived for at least onegenotype of the population of genotypes becomes more fit; and whereinthe plurality of gene classes include a first type of gene class wheregenes in the first class are optimized for each domain, and theplurality of gene classes including a second type of gene class wheregenes in the second type of class are optimized across a plurality ofdomains, and the plurality of gene classes including a third type ofgene class where genes in the third type of class are optimized acrossall domains.
 7. The method of claim 6, further including migrating genesamong the domains by selecting at least one gene from a genotype of acurrent domain population of genotypes and using the selected gene toreplace a corresponding gene in a genotype of another domain populationof genotypes such that the selected gene is incorporated into a genotypeof the genotype population of a different domain.
 8. The method of claim6, further including evolving a next population of genotypes for adomain by selecting at least one genotype from a current populationbased on a fitness level of the genotype and performing a geneticoperation on the at least one genotype, thereby creating at least onenew genotype and adding the at least one new genotype to the nextpopulation.
 9. The method of claim 8, further including repeating thecomparing, and evolving so that for each domain a fittest genotypebecomes increasing more fit.